Semantic Mapping of Environments for Autonomous Devices

ABSTRACT

Methods, systems, and apparatus for receiving a reference to an object located in an environment of a robot, accessing mapping data that indicates, for each of a plurality of object instances, respective probabilities of the object instance being located at one or more locations in the environment, wherein the respective probabilities are based at least on an amount of time that has passed since a prior observation of the object instance was made, identifying one or more particular object instances that correspond to the referenced object, determining, based at least on the mapping data, the respective probabilities of the one or more particular object instances being located at the one or more locations in the environment, selecting, based at least on the respective probabilities, a particular location in the environment where the referenced object is most likely located, and directing the robot to navigate to the particular location.

TECHNICAL FIELD

This application relates generally to environment mapping.

BACKGROUND

Robotic mapping concerns the creation and use of environment mappings byrobotic systems, such as autonomous robots. An environment, such as aproperty, can be represented using a two-dimensional (2D) plan mappingor a three-dimensional (3D) space mapping that provides a 3Drepresentation of the interior of the environment. Autonomous robotsutilize environment mappings to perform object location, path planning,and navigation within an environment, as well as to interact withobjects within the environment. Given a location of the autonomous robotwithin the environment property and a location of a destination withinthe environment, such as a location of a target object in theenvironment, the autonomous robot can plan a path to navigate throughthe property to its destination.

SUMMARY

This specification relates to systems, methods, devices, and othertechniques for mapping the locations of objects within an environment,and to generating a mapping of objects within the environment thatspecifies the probabilities of the objects being located in specificlocations at specific points in time. Environment mappings that identifyspecific object instances and time-varying probabilities of objectinstances being in particular locations within the property are referredto as lifelong semantic mappings. For example, a lifelong semanticmapping of a property may indicate a relatively certain and staticprobability for a location of a wall, since walls are not likely to moveover time, while the lifelong semantic mapping may indicate that thecertainty of a location of a cup in the property decreases rapidly overtime, eventually tending towards zero. Applications of lifelong semanticmapping techniques can enable robotic systems, such as autonomousrobots, to locate specific objects within an environment, and tonavigate through an environment, with improved accuracy.

As referred to herein, semantic mapping generally refers to a mappingprocess that considers characteristics of specific object instances orclasses of objects in determining and predicting the locations of objectinstances within an environment. A semantic mapping of an environmentmay identify each object instance in an environment. Based on thecharacteristics of each object instance or a class corresponding to theobject instance, the semantic mapping may specify one or moreprobabilities of the object instance being located in particularlocations within the environment. A robotic system can use the semanticmapping to locate the object instance within the environment, forexample, by determining to look for the object instance in the mostprobable locations within the environment first, before looking inlocations where the object instance is less likely to be located.

For example, a semantic mapping of a property may include a televisionobject instance and a coffee mug object instance. Locations of thetelevision and the coffee mug may be estimated based on observations byone or more autonomous robots at the property. The locations of thetelevision and the coffee mug in the property may be represented asprobability functions indicating the each object's likelihood of beinglocated in a particular location, where the probability functionsconsider characteristics of each object or their relationship to otherobjects. For example, because a television is a class of object that isnot frequently moved, the probability of the television being locatedwhere it was last seen by an autonomous robot may go relativelyunchanged over time. In contrast, because a coffee mug is a class ofobject that is frequently moved, the probability of the coffee mug beinglocated where it was last seen by an autonomous robot may decreaserelatively quickly over time.

In some implementations, a semantic mapping of an environment mayspecify multiple probabilities for a particular object instance. Eachprobability may correspond to a particular location within theenvironment where the object instance may be located. For example, twoautonomous robots may each detect an object that they each recognize asa particular object instance in different locations within a propertyaround the same time. Since it is likely that one identification of theobject instance was accurate while the other was an erroneousidentification of the object instance, a semantic mapping of theproperty may specify two probabilities for the object instance.

Each probability may be a probability function that indicates thelikelihood that the object instance is located in the correspondinglocation within the property, and may be determined based on, forexample, a confidence of the identification of the object by theautonomous robot, characteristics of the object instance or the class ofthe object, such as how often the object instance is moved or seen at aparticular location within the property, or other information, such asthe proximity of the object instance to another related object instance.

A semantic mapping of an environment that provides one or moreprobabilities corresponding to potential locations of a particularobject instance can improve the capabilities of a robotic system tolocate objects within an environment. For example, rather than a staticmapping of the environment that does not change over time, new staticmappings may be generated that replace older mappings over time as newobservations of the environment are made. In some implementations, anage of a mapping may even be considered in determining the likelihood ofthe mapping being accurate. However, such mappings do not distinguishbetween particular object instances located within the environment, nordo they consider alternate possible locations of an object instancewithin an environment, or the likelihood of the object instance beinglocated at each of those possible locations.

For example, a static mapping of a property may not consider thetransient nature of a coffee mug versus a television, or providealternate possible locations of the coffee mug within the property. If arobotic system relying on the static mapping fails to locate the coffeemug within the property, i.e., at the location indicated by the staticmapping, the robotic system is forced to aimlessly search for the coffeemug within the property until it finds it. In contrast, a semanticmapping as disclosed herein may provide the robotic system withalternate locations where the coffee mug might be located within theproperty, and a probability of the coffee mug being located in anyparticular location may inform the scope of the robotic system's searchfor the coffee mug within the property.

Innovative aspects of the subject matter described in this specificationmay be embodied in methods, systems, and computer-readable devicesstoring instructions configured to perform the actions of receiving, bya system configured to facilitate operation of a robot in anenvironment, a reference to an object located in the environment of therobot, accessing, by the system, mapping data that indicates, for eachof a plurality of object instances, respective probabilities of theobject instance being located at one or more locations in theenvironment, wherein the respective probabilities of the object instancebeing located at each of the one or more locations in the environmentare based at least on an amount of time that has passed since a priorobservation of the object instance was made, identifying, by the systemand from among the plurality of object instances, one or more particularobject instances that correspond to the referenced object, determining,by the system and based at least on the mapping data, the respectiveprobabilities of the one or more particular object instances beinglocated at the one or more locations in the environment, selecting, bythe system and based at least on the respective probabilities of the oneor more particular object instances being located at the one or morelocations in the environment, a particular location in the environmentwhere the referenced object is most likely located, and directing, bythe system, the robot to navigate to the particular location.

These and other embodiments may each optionally include one or more ofthe following features. In various examples, each of the plurality ofobject instances is an instance of an object class; the features includecontrolling, by the system, navigation of the robot to the particularlocation; the respective probabilities of an object instance beinglocated at one or more locations in the environment are based at leaston a temporal characteristic associated with an object class of theobject instance; the respective probabilities of an object instancebeing located at one or more locations in the environment are based atleast on a spatial relationship between the object instance and one ormore other object instances; the respective probabilities of an objectinstance being located at one or more locations in the environment arebased at least on a relationship between an object class of the objectinstance and an area type of a location associated with the probability;the respective probabilities of an object instance being located at oneor more locations in the environment are based at least on aspatiotemporal relationship between an object class of the objectinstance and a location associated with the probability; the respectiveprobabilities of an object instance being located at one or morelocations in the environment are based at least on an identificationconfidence associated with one or more prior observations of the objectinstance; the mapping data is generated based on observations of aplurality of robots; the system accesses the mapping data at acloud-based computing system.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other potential features, aspects, and advantages ofthe subject matter will become apparent from these description, thedrawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1B depict example systems for mapping an environment.

FIG. 2 depicts example factors for providing lifelong semantic mapping.

FIG. 3 depicts an example system for determining the location of anobject instance within an environment.

FIG. 4 is a flowchart of an example process for determining the locationof an object instance within an environment.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

A robotic mapping of an environment, such as a property, can berepresented using a two-dimensional (2D) plan mapping or athree-dimensional (3D) space mapping that provides a 3D representationof the interior of the environment. Environment mappings may be metricrepresentations, in which structures and objects within the environmentare associated with coordinate locations or boundaries, or may betopological representations in which structures and objects are definedbased on relationships between them, such as the distances and anglesbetween the various objects and boundaries within the environment.Environment mappings may represent the free space within an environment,i.e., the areas of the environment where an autonomous robot ispermitted to move, may represent objects within an environment, i.e.,the areas of the environment that are occupied by other structures orobjects and therefore represent where an autonomous robot is notpermitted to move, or may be composite mappings that represent both thefree space and objects within an environment.

Environment mappings, once determined, can be maintained as staticmappings that do not change over time. In some implementations, a levelof confidence that an environment mapping is accurate may be determinedbased on how recently the mapping was generated or how recentlyinformation used to generate the mapping was obtained. For example, amapping of a property that is generated based on dated information, suchas observations by an autonomous robot that occurred several months ago,may have an associated confidence that is lower compared to a mapping ofthe same property that is generated based on more recent information,such as observations by an autonomous robot occurring only several daysago.

Autonomous robots utilize environment mappings to perform objectlocation, path planning and navigation within an environment. Given alocation of the autonomous robot within the environment property and alocation of a destination within the environment, such as a location ofa target object in the environment, the autonomous robot can plan a pathto navigate through the property to its destination. In some instances,a robot's location within an environment can be provided to theautonomous robot, for example, by another system or a human user. Inother implementations, an autonomous robot performs localization todetermine its location within the environment. Localization istraditionally performed, for example, by determining the proximity ofthe autonomous robot to one or more proximity beacons, using near-fieldcommunication NFC), based on detecting a particular wireless freeinternet (WiFi) network or the strength of a particular WiFi network,using visible light communication (VLC), or based on detecting aparticular Bluetooth connection or strength of Bluetooth connection.

However, even with accurate localization, an autonomous robot that isrelying on an outdated or uncertain mapping of an environment may havedifficulty in performing tasks due to a failure to locate target objectswithin the environment. For example, if a mapping of an environment issufficiently outdated such that an autonomous robot has very limitedconfidence in the mapping, the robotic system may have difficultynavigating through and locating objects within the environment.

To address this, a robotic mapping as described herein may considercharacteristics of specific objects within an environment to provide alifelong mapping of the environment, that is, a mapping that considersthe typical usage and characteristics of objects within an environment.By doing so, the robotic mapping can more accurately describe thelocations of specific objects within the mapping. Specifically, becauseobjects may have different characteristics in terms of their usefullifetime, i.e., how long a user of the property keeps an object beforedisposing of it, their mobility, i.e., how often users move an object,their relationships to other objects, etc., modeling each object'slocation within a space can improve the ability of a robot to predictthe location of an object within an environment and to navigate throughthe environment to that object.

FIG. 1A depicts an example system 100 for generating and using alifelong semantic mapping of an environment. Generally, the system 100includes one or more autonomous robots 105 a, 105 b located at anenvironment 102, such as a residential or commercial property. Theautonomous robots 105 a, 105 b are in communication with a lifelongsemantic mapping engine 110. The autonomous robots 105 a, 105 bcommunicate with the lifelong semantic mapping engine 110 over one ormore wired or wireless communication links, for example, over one ormore wired or wireless local area networks (LANs) or wide area networks(WANs).

As shown in FIG. 1A, the lifelong semantic mapping engine 110 stores asemantic mapping 115 of the environment 102 that specifies the locationsof object instances, including, for example, a counter, sink, stove,refrigerator, sofa, table, and television within the environment 102.Each of the object instances represented in the semantic mapping 115 isassociated with a probability of the object instance being located at aparticular location within the environment 102. For example, each objectinstance represented in the semantic mapping 115 may be associated witha corresponding probability function that indicates the probability ofthe object instance being located in a particular location at any giventime.

The lifelong semantic mapping engine 110 can update the semantic mapping115 based on information received from the autonomous robots 105 a, 105b. For example, each of the autonomous robots 105 a, 105 b may beequipped with one or more sensors or cameras for obtaining observationsfrom the environment 102. The autonomous robots 105 a, 105 b maytransmit observation data corresponding to sensor, image, or video datacaptured by the sensors or cameras of the autonomous robots 105 a, 105 bto the lifelong semantic mapping engine 110. Based on the observationdata, the lifelong semantic mapping engine 110 can update the semanticmapping 115 to more accurately reflect the configuration of theenvironment 102.

In some instances, an autonomous robot 105 a, 105 b only transmitsobservation data to the lifelong semantic mapping engine 110 if theautonomous robot 105 a, 105 b determines that its observation issufficiently significant to warrant transmission to the lifelongsemantic mapping engine 110, i.e., that the observation is important andshould therefore be addressed by or incorporated into the semanticmapping 115. For example, an observation that a table has been angledslightly but is generally in the same location may not be significantand may therefore not be an observation that is transmitted to thelifelong semantic mapping engine 110. However, an observation of a plantthat was not previously included in the semantic mapping 115 may beconsidered sufficiently important by the autonomous robot 105 a, 105 bto warrant providing observation data indicating the plant to thelifelong semantic mapping engine 110. Based on the lifelong semanticmapping engine 110 receiving observation data from the autonomous robot105 a, 105 b, the lifelong semantic mapping engine 110 can update thesemantic mapping 115 to include the plant.

In other implementations, the lifelong semantic mapping engine 110 mayreceive other information in addition to the observation data providedby the autonomous robots 105 a, 105 b, and may update the semanticmapping 115 based on the other information. The other information mayinclude, for example, user corrections or object rules provided by usersof the environment 102. The other information may be received by thelifelong semantic mapping engine 110 from the autonomous robots 105 a,105 b, or from another system, such as from another server that is incommunication with the lifelong semantic mapping engine 110.

For example, a user of the environment 102 may provide a request to anautonomous robot 105 a, 105 b that identifies a particular objectinstance. The autonomous robot 105 a, 105 b may proceed to navigate to alocation of an object instance that it identifies, based on its localsemantic mapping 125 a, 125 b, to be the location of the particularobject instance. The user of the environment 102 may then correct theautonomous robot 105 a, 105 b to indicate that the object instance thatthe autonomous robot 105 a, 105 b identified is not the particularobject instance requested, and optionally identifies a different objectinstance as the particular object instance requested. In response, theautonomous robot 105 a, 105 b can update its local semantic mapping 125a, 125 b to include the correction, and may also transmit information tothe lifelong semantic mapping engine 110 indicating the correction sothat the lifelong semantic mapping engine 110 can update the semanticmapping 115 accordingly.

Similarly, the user of the environment 102 may provide rules regardingspecific object instances within the environment 102. For example, auser of the environment 102 may indicate that a particular objectinstance, such as a coffee mug or laptop computer, should be moved fromone location within the environment 102 to another location within theenvironment 102 at a particular time of day, e.g., from a living roomwhere the user might use the laptop computer in the evening, to a homeoffice where they use the laptop computer during the day. The user mayprovide such a rule either directly to the autonomous robot 105 a, 105b, or at another system, such as at an interface for the system 100 thatis provided at another computer system. Information indicating the rulemay be provided to the lifelong semantic mapping engine 110, forexample, from the autonomous robot 105 a, 105 b to which the userprovided the rule, so that the lifelong semantic mapping engine 110 canupdate the semantic mapping 115.

Updating the semantic mapping 115 may include adding new objectinstances observed by the autonomous robots 105 a, 105 b in theenvironment 102 to the semantic mapping 115, may include adjusting thelocations of object instances in the semantic mapping 115 based onobservations by the autonomous robots 105 a, 105 b, or can includeremoving object instances from the semantic mapping 115 based onobservations by the autonomous robots 105 a, 105 b. The lifelongsemantic mapping engine 110 can also modify the semantic mapping 115 byadjusting probabilities associated with object instances represented inthe semantic mapping 115. For example, if an autonomous robot 105 a, 105b observes a table object in a location of the environment 102, theautonomous robot 105 a, 105 b may transmit observation data to thelifelong semantic mapping engine 110 indicating that observation. Inresponse, the lifelong semantic mapping engine 110 may increase aprobability or adjust a probability function associated with the tableto indicate that the table is most likely located in that particularlocation.

In some implementations, the lifelong semantic mapping engine 110continuously updates the semantic mapping 115 as observation data isreceived from the autonomous robots 105 a, 105 b. In otherimplementations, the lifelong semantic mapping engine 110 mayperiodically updated the semantic mapping 115, or may update thesemantic mapping 115 once a threshold amount of observation data or athreshold number of changes to the semantic mapping 115 are required.

The lifelong semantic mapping engine 110 can provide an updated semanticmapping 115 to each of the autonomous robots 105 a, 105 b. For example,the lifelong semantic mapping engine 110 may update the semantic mapping115 and provide the updated semantic mapping 115 to each or a subset ofthe autonomous robots 105 a, 105 b. The autonomous robots 105 a, 105 breceive mapping data corresponding to the semantic mapping 115 and storelocal semantic mappings 125 a, 125 b that correspond to the semanticmapping 115. For example, the lifelong semantic mapping engine 110 maycontinuously or periodically update the semantic mapping 115. Thelifelong semantic mapping engine 110 may provide mapping datacorresponding to the updated semantic mapping 115 to the autonomousrobots 105 a, 105 b, such that each of the autonomous robots 105 a, 105b can receive the semantic mapping 115 and store the semantic mapping115 as a local semantic mapping 125 a, 125 b.

After storing the semantic mapping 115 as a local semantic mapping 125a, 125 b, each autonomous robot 105 a, 105 b can independently updateits local semantic mapping 125 a, 125 b based on its observations. Byupdating its local semantic mapping 125 a, 125 b based on localobservations, the autonomous robot 105 a, 105 b can interact withobjects in the environment 102 with improved accuracy. Updates made tothe local semantic mapping 125 a, 125 b may be later provided to thelifelong semantic mapping engine 110 as observation data for updatingthe semantic mapping 115. In some implementations, as discussed above,the autonomous robots 105 a, 105 b may transmit all or only a portion ofthe updates to its local semantic mapping 125 a, 125 b to the lifelongsemantic mapping engine 110 to incorporate those updates into thesemantic mapping 115, based on the importance of the updates to thelocal semantic mapping 125 a, 125 b.

For example, the lifelong semantic mapping engine 110 of FIG. 1A maygenerate a local semantic mapping 115 based on observation data receivedfrom each of the autonomous robots 105 a, 105 b located in theenvironment 102. The semantic mapping 115 may include, as shown, objectinstances for a counter and sink, stove, refrigerator, couch, table, andtelevision, along with information defining boundaries and walls in theenvironment 102. The lifelong semantic mapping engine 110 can providethe semantic mapping 115 to each of the autonomous robots 105 a, 105 b,and each of the autonomous robots 105 a, 105 b can store the semanticmapping 115 as a local semantic mapping 125 a, 125 b.

Each autonomous robot 105 a, 105 b can then make updates as needed toits local semantic mapping 125 a, 125 b, based on their observations.Because of the location of the autonomous robot 105 a in the environment102, the autonomous robot 105 a does not perceive any changes to theenvironment 102, and therefore does not make any updates to its localsemantic mapping 125 a. However, the autonomous robot 105 b located in aliving area of the environment 102 may observe a plant object instancethat is not represented in the semantic mapping 115. Based on itsobservation, the autonomous robot 105 b may update its local semanticmapping 125 b to include an object instance corresponding to the plant.The autonomous robot 105 b may further determine that the presence ofthe plant object instance in the living area of the environment 102 is asubstantial change to the mapping of the environment 102, and maytherefore provide observation data to the lifelong semantic mappingengine 110 indicating the location of the plant object instance. Basedon the information, the lifelong semantic mapping engine 110 can updatethe semantic mapping 115 to include the plant object instance, as shownby the dotted lines in the semantic mapping 115 of FIG. 1A.

FIG. 1B depicts the example system 100 of FIG. 1A with additionaldetail. Specifically, FIG. 1B depicts an autonomous robot 105 incommunication with the lifelong semantic mapping engine 110 over one ormore wired or wireless networks, such as one or more LANs or WANs.Briefly, components of the autonomous robot 105 include one or moresensors 150, an object recognition engine 152, a mapping update engine154, mapping storage 156, a controller 158 configured to control theautonomous robot 105, and one or more actuators 160 for effectingmovement of the autonomous robot 105 and interaction of the autonomousrobot 105 with objects in the environment 102. Components of thelifelong semantic mapping engine 110 include an update evaluation engine112, an environment mapping database 114, and a mapping distributor 116.

While each of the components shown in FIG. 1B are shown as beinginternal to the autonomous robot 105 or the lifelong semantic mappingengine 110, in some implementations, components of either the autonomousrobot 105 or lifelong semantic mapping engine 110 can be external to theautonomous robot 105 or lifelong semantic mapping engine 110. Forexample, the mapping storage 156 of the autonomous robot 105 may belocated at a cloud-based storage environment accessible by theautonomous robot 105, or the environment mapping database 114 of thelifelong semantic mapping engine 110 may be located at a remote storageaccessible to the lifelong semantic mapping engine 110. In someimplementations, one or more of the components depicted may be combinedinto a single component or may be included elsewhere in the system 100.For example, in some implementations, the object recognition engine 152of the autonomous robot 105 may be located at the lifelong semanticmapping engine 110.

The autonomous robot 105 and lifelong semantic mapping engine 110 cancommunicate to perform lifelong semantic mapping of the environment 102of FIG. 1A. In the scenario shown in FIG. 1B, the lifelong semanticmapping engine 110 maintains a semantic mapping of the environment 102at the environment mapping database 114, e.g., the semantic mapping 115of FIG. 1A. The lifelong semantic mapping engine 110 has provided theautonomous robot 105 with the semantic mapping of the environment 102 toallow the autonomous robot 105 to navigate through and interact with theenvironment 102. The operations depicted in FIG. 1B enable theautonomous robot 105 to update its local semantic mapping of theenvironment 102 and to provide observation data to the lifelong semanticmapping engine 110 to permit updating of the semantic mapping stored atthe environment mapping database 114 of the lifelong semantic mappingengine 110.

At stage (A) the one or more sensors 150 of the autonomous robot 105transmit sensor data to the object recognition engine 152 of theautonomous robot 105. In some implementations, the one or more sensors150 may include one or more devices capable of taking measurements ofthe environment, for example, one or more stereo cameras, lightdetection and ranging (LIDAR) sensors, sonar, radar, cameras or videocameras, or other forms of imaging or depth detection. The one or moresensors 150 can obtain measurements from the environment and informationindicating locations within the environment 102 corresponding to themeasurements, e.g., a location and orientation of the autonomous robot105 from where the measurements were taken, or locations of the objectsbeing detected by the one or more sensors 150.

For example, each measurement may indicate a location from which themeasurement was taken by the autonomous robot 105, such as coordinates,latitude and longitude, or other location information that indicates aposition of the autonomous robot 105 within the environment 102. Theinformation may also indicate an orientation corresponding to themeasurement, such as an indication of a direction from which themeasurement was taken and an angle from which the measurement was taken.The measurements taken by the autonomous robots 105 may include asufficient number of measurements to generate a 2D or 3D mapping of theobserved portion of the environment 102.

The object recognition engine 152 can receive the sensor data and canidentify objects in the environment 102 based on the received sensordata. The object recognition engine 152 may identify objects based onthe geometry of objects depicted or described by the sensor data. Forexample, the object recognition engine 152 may have access to one ormore object templates or object feature templates that specify featuresof classes of objects or specific object instances. The objectrecognition engine 152 may compare features derived from the sensor datato the templates to identify object instances depicted or described bythe sensor data.

In some implementations, object classes or instances may be described byobject constellation models, in which the object classes or instancesare represented by features that are geometrically related. For example,an object constellation model may describe the positioning of specificfeatures of object instances relative to one another. The objectrecognition engine 152 may identify an object based on identifying thefeatures of a particular object and determining that the position ofthose features relative to one another satisfies the objectconstellation model.

The object recognition engine 152 may consider other information inidentifying objects based on the sensor data received from the one ormore sensors 150. For example, the object recognition engine 152 mayconsider the likely positioning of an object within a particular orenvironment. For instance, an object that resembles both a table and acabinet but that is attached to a floor may be identified as a tableobject instance, since it is more likely that a table would be connectedto a floor than would a cabinet. The object recognition engine 152 mayalso consider the proximity of other identified object instances whenidentifying new object instances. For example, an object that could beidentified as either a television or a microwave but that is positionednear a refrigerator object instance may be identified as a microwave,since it is more likely that a microwave will be near a refrigeratorthan a television. Other methods of object recognition may beimplemented by the object recognition engine 152.

In some implementations, the object recognition engine 152 may haveaccess to information identifying object instances previously identifiedin the environment 102. Access to such information may enable the objectrecognition engine 152 to identify a particular object instance in theenvironment 102 that has been previously identified in the environment102. For example, the object recognition engine 152 may have access tothe local semantic mapping stored at the mapping storage 156 of theautonomous robot 105, or may have access to a database that describeseach object instance identified in the environment 102. The objectrecognition engine 152 may compare features of object instances detectedin the environment 102 to features of object instances previouslyidentified in the environment 102 to determine whether the autonomousrobot 105 has observed an object instance that was previously observedin the environment 102.

At stage (B), the object recognition engine 152 provides information tothe mapping update engine 154 indicating object instances that itdetected in the environment 102, and locations of the object instanceswithin the environment 102. For example, based on the object recognitionengine 152 determining that the one or more sensors 150 of theautonomous robot 105 observed a plant in the environment 102, the objectrecognition engine 152 can transmit data to the mapping update engine154 that indicates features about the plant object instance, including alocation of the plant object instance in the environment 102.

The mapping update engine 154 can receive the information from theobject recognition engine 152, and can update the local semantic mappingof the environment 102 stored at the mapping storage 156 of theautonomous robot 105. For example, the mapping update engine 154 mayaccess the local semantic mapping stored at the mapping storage 156 ofthe autonomous robot 105, and determine that the local semantic mappingdoes not include a plant object instance corresponding to the detectedplant instance, or may determine that the local semantic mappingincludes a plant object instance corresponding to the detected plantobject instance, but in a different location within the environment 102.

Based on these determinations, at stage (C), the mapping update engine154 can transmit data to the mapping storage 156 to update the localsemantic mapping stored at the mapping storage 156 of the autonomousrobot 105. For example, the mapping update engine 154 may transmit datato the mapping storage 156 that causes the mapping storage 156 toaugment the local semantic mapping with the newly detected objectinstance. Adding the object instance to the local semantic mapping mayinvolve, for example, updating a 2D or 3D mapping of the environment 102to include the object instance, e.g., in a mesh mapping of theenvironment 102. The portion of the 2D or 3D mapping of the environment102 corresponding to the identified object may be labelled as theparticular object instance.

Adding the object instance to the local semantic mapping may alsoinclude assigning a probability to the object instance indicating thelikelihood of the object instance being located there. For example, themapping storage 156 or another component of the system 100 may determinea probability function indicating the likelihood of the object instancebeing located at the identified location in the environment 102 overtime, based on characteristics of the object instance.

If the object instance is one that was previously detected in theenvironment 102, such that the object instance is already represented inthe local semantic mapping of the environment 102, then the mappingstorage 156 may simply update the local semantic mapping based on thenew observation of the object instance by the autonomous robot 105. Forexample, if a plant object instance was previously observed in adifferent location in the environment 102, then an observation of theplant object instance in a different location in the environment 102 maycause the mapping storage 156 or another component to update theprobability function associated with the plant object instance toindicate that the different location is the most likely location of theplant object instance in the environment 102.

At stage (D), the mapping update engine 154 can transmit information tothe lifelong semantic mapping engine 110 that indicates one or moreobservations of the autonomous robot 105 that may be incorporated intothe semantic mapping stored at the lifelong semantic mapping engine 110.Specifically, the mapping update engine 154 can determine whether one ormore of the objects recognized by the object recognition engine 152, orlocations of objects recognized by the object recognition engine 152, issubstantial and therefore should be incorporated into the semanticmapping stored by the lifelong semantic mapping engine 110. For example,the mapping update engine 154 may determine that one or more of theobjects identified by the object recognition engine 152 are not alreadyincluded in the local semantic mapping stored at the mapping storage 156of the autonomous robot 105, or that one or more of the identifiedobjects is in a different location than that indicated in the localsemantic mapping 105. In response to this determination, the mappingupdate engine 154 can transmit information, e.g., the observation dataidentified in FIG. 1A, to the update evaluation engine 112 of thelifelong semantic mapping engine 110.

While in some implementations the mapping update engine 154 may evaluateobservations by the autonomous robot 105 to determine whether theobservations are sufficiently important to warrant their incorporationinto the semantic mapping stored by the lifelong semantic mapping engine110, in other implementations, the mapping update engine 154 may provideall of the observations of the autonomous robot 105 to the lifelongsemantic mapping engine 110. In such an implementation, the mappingupdate engine 154 or another component of the autonomous robot 105,e.g., the object recognition engine 152, can transmit all observations,e.g., all recognitions of object instances and their locations at theenvironment 102, to the update evaluation engine 112 of the lifelongsemantic mapping engine 110. This removes the need for the mappingupdate engine 154 to evaluate the importance of specify observations bythe autonomous robot 105, instead allowing the lifelong semantic mappingengine 110 to determine which observations are sufficiently important towarrant incorporation into the semantic mapping stored at theenvironment mapping database 114.

The update evaluation engine 112 receives the information from themapping update engine 154 that indicates the observations of theautonomous robot 105, and may evaluate the observations to determinewhether and how those observations should be incorporated into thesemantic mapping of the environment 102 stored at the environmentmapping database 114. Based on these determinations, at stage (E), theupdate evaluation engine 112 can transmit data to the environmentmapping engine to update the semantic mapping of the environment 102stored at the environment mapping database 114.

For example, the update evaluation engine 112 may receive from themapping update engine 154 observation data that indicates one or moreobject instances detected by the autonomous robot 105 in the environment102, as well as locations of the object instances within the autonomousrobot 105. The update evaluation engine 112 may access the semanticmapping stored at the environment mapping database 114, and based on theobservation data may determine to update the semantic mapping. Updatingthe semantic mapping stored at the environment mapping database 114 mayinclude adding object instances to the semantic mapping, settingprobabilities associated with newly added object instances in thesemantic mapping, or adjusting probabilities associated with existingobject instances represented in the semantic mapping.

For instance, the update evaluation engine 112 of the lifelong semanticmapping engine 110 may receive observation data from the mapping updateengine 154 of the autonomous robot 105 identifying a plant objectinstance in the environment 102 and a table object instance in theenvironment 102. The update evaluation engine 112 may access thesemantic mapping stored at the environment mapping database 114, and maydetermine whether the plant and table object instances are representedin the semantic mapping of the environment 102.

Based on determining that the plant object instance is not representedin the semantic mapping stored at the environment mapping database 114,the update evaluation engine 112 may update the semantic mapping storedat the environment mapping engine to include the plant object instanceat the location where it was observed by the autonomous robot 105. Theupdate evaluation engine 112 may also associate a probability with theplant object instance, e.g., a probability function that indicates theprobability of the plant object instance being located at the particularlocation within the environment 102 over time. In determining theprobability, the update evaluation engine 112 may consider a number offactors. For example, the update evaluation engine 112 may determinethat a house plant class of object instances tend to remain in the samelocation within a property, which may be reflected in the probabilityfunction of the plant object instance such that he probability of theplant being located at the identified location decreases relativelyslowly over time. Other characteristics of the plant object instance maybe considered in determining the probability of the plant objectinstance being located at the particular location within the property,as discussed in further detail with respect to FIGS. 2 through 4.

The update evaluation engine 112 may perform a similar evaluation withrespect to the table object instance identified in the environment 102.For example, the update evaluation engine 112 may access the semanticmapping at the environment mapping database 114 and determine that thetable object instance is already included in the semantic mapping. Theupdate evaluation engine 112 may compare the location of the tableobject instance identified in the observation data received from theautonomous robot 105 with a location within the environment 102associated with the table object instance that is specified by thesemantic mapping. If the location indicated by the observation data isgenerally the same as the location indicated by the semantic mapping,then the observation operates to reinforce the table object instancebeing located at that location. Therefore, the update evaluation engine112 may update a probability associated with the table object instancein the semantic mapping.

For example, the update evaluation engine 112 may update a probabilityfunction indicating the likelihood of the table object instance beinglocated in that location over time to increase the probability of thetable object being located in that location, and adjusting the functionso that the probability of the table being located there decreases moreslowly over time, since it appears that the location of the table objectinstance is relatively static. Alternatively, if the location where theautonomous robot 105 observed the table object instance is inconsistentwith a previous location where the table object instance was located,then the update evaluation engine 112 may add a second instance of thetable object instance to the semantic mapping. For example, the updateevaluation engine 112 may associate the new location with the tableobject instance, and may assign a probability to that location, suchthat the table object instance in the semantic mapping is associatedwith two possible locations, each with a corresponding probability ofthe table object instance being located there. Alternatively, the updateevaluation engine 112 may create a new table object instance in thesemantic mapping and associate a probability with that table objectinstance, such that the semantic mapping includes two identical tableobject instances, each with a corresponding probability.

The update evaluation engine 112 may also determine that certainobservations of the autonomous robot 105 indicated in the observationdata received from the mapping update engine 154 are insufficient tojustify updating the semantic mapping stored at the environment mappingdatabase 114. For example, if the observation data indicates that arelatively stationary object instance, such as a couch, is detected asbeing in the same location, the update evaluation engine 112 may forgoupdating the semantic mapping to reflect that observation. Similarly, ifan observation of the autonomous robot 105 has a low confidence of beingaccurate, due to the distance from which the autonomous robot 105 maythe observation or otherwise, then the update evaluation engine 112 maydetermine not to update the semantic mapping to reflect thatobservation.

At stage (F), the mapping distributor 116 can access the updatedsemantic mapping at the environment mapping database 114. For example,the mapping distributor 116 may be configured to provide autonomousrobots such as the autonomous robot 105 with update semantic mappings ofthe environment 102. The mapping distributor 116 may be configured to doso periodically, whenever changes are made to the semantic mappingstored at the environment mapping database 114, in response to a requestfrom an autonomous robot such as the autonomous robot 105, based ondetermining that a new autonomous robot has been added to the system100, or based on other conditions. The mapping distributor 116 canaccess the updated semantic mapping stored at the environment mappingdatabase 114, and can identify one or more autonomous robots to receivethe update semantic mapping.

The mapping distributor 116 can then transmit the updated semanticmapping to the mapping storage 156 at stage (G). For example, themapping distributor 116 can transmit the updated semantic mapping to oneor more identified autonomous robots, including the autonomous robot105, over one or more wired or wireless networks, such as one or moreLANs or WANs. The mapping storage 156 of the autonomous robot 105 canreceive the updated semantic mapping from the mapping distributor 116,and can store the updated semantic mapping at the mapping storage 156.

In some implementations, storing the updated semantic mapping at themapping storage 156 can involve replacing the local semantic mappingstored at the mapping storage 156 with the updated semantic mapping. Inother implementations, the mapping storage 156 or another component ofthe autonomous robot 105 may update the local semantic mapping stored atthe mapping storage 156 based on the updated semantic mapping receivedfrom the mapping distributor 116. For example, the mapping storage 156or another component of the autonomous robot 105 may compare the updatedsemantic mapping to the local semantic mapping stored at the mappingstorage 156 to identify differences between the local semantic mappingand the updated semantic mapping. The mapping storage 156 or anothercomponent of the autonomous robot 105 can update the local semanticmapping to incorporate features of the update semantic mapping whereappropriate.

In some examples, updating the local semantic mapping may allow theautonomous robot 105 to operate with greater accuracy, since the localsemantic mapping may include some features or object instances that arenot identified in the updated semantic mapping. For instance, the localsemantic mapping may include features, such as object instances, thathave been added to the local semantic mapping based on observations bythe autonomous robot 105 of the environment immediately surrounding theautonomous robot 105. By updating the local semantic mapping rather thanreplacing the local semantic mapping with the updated semantic mapping,the autonomous robot 105 may maintain its ability to operate in itscurrent location within the environment 102 with precision.

After updating the local semantic mapping stored at the mapping storage156 of the autonomous robot 105, the autonomous robot 105 can use theupdated local semantic mapping to navigate through and interact with theenvironment 102. To do so, at stage (H), a controller 158 of theautonomous robot 105 may access the updated local semantic mapping atthe mapping storage 156. The controller 158 may be a processor or othercomponent capable of generating instructions to control one or moreactuators 160 of the autonomous robot 105.

For example, the controller 158 may be a component configured to receivean instruction for the autonomous robot 105 to navigate to the locationof a particular object instance in the environment 102. To generateinstructions to control the autonomous robot 105 to navigate to thatlocation, the controller 158 may access the updated local semanticmapping at the mapping storage 156. The controller 158 may determine,from the updated local semantic mapping of the environment 102 aparticular location in the environment 102 where the identified objectinstance is most likely located. For example, the controller 158 maydetermine, based on a probability function associated with theparticular object instance and a current time, a most probable locationof the particular object instance within the environment 102. Thecontroller 158 may also determine a current location of the autonomousrobot 105 within the environment 102, and based on the current locationof the autonomous robot 105 and the most likely location of theparticular object instance in the environment 102, may determine a pathof movement of the autonomous robot 105 to navigate to the location ofthe particular object instance. The controller 158 may generateinstructions for controlling actuators 160 of the autonomous robot 105to navigate the autonomous robot 105 to the location of the particularobject instance.

At stage (I), the controller 158 may control actuators 160 of theautonomous robot 105 using the generated instructions. The one or moreactuators 160 may include, for example, one or more transport mechanismsof the autonomous robot 105, e.g., wheels, tread, legs, or othertransportation mechanism of the autonomous robot 105, and also includeone or more actuators 160 for controlling the autonomous robot 105 tointeract with objects in the environment, such as an arm, claw, orsuction cup of the autonomous robot 105. The actuators 160 may alsoinclude actuators 160 for allowing the autonomous robot 105 to observethe environment 102, for example, actuators 160 for controlling thepositioning of cameras or other of the one or more sensors 150 of theautonomous robot 105. For example, based on the controller 158generating instructions to navigate the autonomous robot 105 to thelocation of a particular object instance in the environment 102, thecontroller 158 can control one or more wheels of the autonomous robot105 to navigate the autonomous robot 105 to the location of theparticular object instance.

FIG. 2 depicts examples of factors that may be considered in providinglifelong semantic mapping. Specifically, FIG. 2 depicts an environment202 including a number of object instances. Each object instance may berepresented in a semantic mapping of the environment 202, e.g., asemantic mapping stored at an environment mapping database 114 of anlifelong semantic mapping engine 110, as discussed with respect to FIG.1B. Each object instance represented in the semantic mapping of theenvironment 202 may also be associated with one or more probabilitiesthat each indicate the likelihood of the object instance being in aparticular corresponding location within the environment 102.

Each probability may be represented as a probability function, such as alogistic, logarithmic, exponential, Gaussian, linear, or other function.A probability function associated with a particular object instance andlocation within the environment 202 may indicate the probability of theparticular object instance being located at the location within theenvironment 202 over time. The probability function may be determinedbased on a number of factors, including characteristics of theparticular object instance, a class of the particular object instance,relationships between the particular object instance and other objectinstances, relationships between the particular object instance andlocations within the environment 202, may be determined based on aconfidence of identification of the particular object instance, may bedetermined based on how recently the particular object instance has beenobserved in the environment 202, or may be determined based on otherfactors.

For example, a semantic mapping of the environment 202 may include acoffee mug 212 and a paper cup 214 that are each associated with one ormore locations within the environment 202. Probabilities associated withthe locations where the coffee mug 212 and paper cup 214 are indicatedas being located within the environment 202 may be determined in partbased on how recently an observation of the coffee mug 212 and paper cup214 at those respective locations took place. For example, if the coffeemug 212 was observed in its location only a few minutes ago, it is muchmore likely that the coffee mug 212 will be at that location than if itwas most recently observed in that location several days prior.

Characteristics of a coffee mug class of objects and of a paper cupclass of object instances may also influence the respectiveprobabilities of the coffee mug 212 and paper cup 214 being located inparticular locations within the environment 202. For example, both thecoffee mug class of object instances and the paper cup class of objectinstances may be associated with an itinerant characteristic thatindicates that object instances of that class move frequently within anenvironment. Because of this characteristic, a probability associatedwith a particular location where the coffee mug 212 or the paper cup 214may be located may generally decrease rapidly over time, as it isunlikely that either object instance will be located in the samelocation for an extended period of time. Thus, if an autonomous robotsuch as the autonomous robot 105 observes either the mug 212 or cup 214,the location where the mug 212 or cup 214 was observed may be assigned aprobability that quickly declines over time, since it is unlikely that,for example, the mug 212 or cup 214 will be located in the same locationseveral hours or days later.

In addition, classes of object instances, such as classes of coffee mugobject instances and of paper cup object instances, may each beassociated with a characteristic that indicates an expected lifetime ofobject instances of that class. Generally, coffee mug object instancesare somewhat permanent object instances within an environment, suchthat, while the location of the coffee mug may change relativelyfrequently, the coffee mug tends to be located somewhere in theenvironment for a long period of time. That is, it is unlikely that auser of the environment will dispose of the coffee mug. However, this isnot the case for paper cup object instances, which tend to both moverelatively frequently within an environment and be quickly disposed ofby users of the environment. Thus, the coffee mug 212 object instancemay be associated with a permanent characteristic and the paper cup 214object instance associated with a temporary characteristic.

Probability functions corresponding to the mug 212 and cup 214 in thesemantic mapping of the environment 202 may differ based on theirexpected lifetime characteristic. While both object instances may havesimilar probabilities over short time spans, due to each being within anitinerant class of object instances, the probability of the paper cup214 being located in the particular location or other locations in theenvironment 202 may decline more quickly over a longer time framecompared to the coffee mug 212, since users of the environment 202 willlikely dispose of the paper cup 214 in a few hours or days, which is notthe case for the coffee mug 212. Thus, as shown in FIG. 2, theprobability of the paper cup 214 object instance being located in aparticular location within the environment 202 may decline rapidlybeyond a particular time, compared with the coffee mug 212 objectinstance that is more likely to remain somewhere within the environment202.

Probabilities associated with particular object instances may alsoreflect observations of the particular object instance over time. Forexample, the system 100 of FIGS. 1A and 1B may record multipleobservations of a particular object over time and, based on the multipleobservations of the object instance over time, determine how frequentlyor how far a particular object instance moves.

Such a concept may be relevant, for example, to the table 216 objectinstance in the environment 202 depicted in FIG. 2. Multipleobservations of the table 216 object instance may indicate that thetable 216 object instance moves frequently, but only by small amounts.For example, the table 216 may be observed as being closer to or furtheraway from a couch within the environment 202, but is also within thisvery limited range of locations. Thus, the table 216 object instance maybe associated with a characteristic indicating that the table 216 haslimited movement in the environment 202, which can then impact theprobability of the table 216 being located in a particular locationwithin the environment 202. For example, a probability function for thetable 216 may reflect that the table 216 has a relatively highprobability of being located in the same location in the environment 202over time, as shown in FIG. 2.

In some implementations, autonomous robots such as the autonomous robot105 a, 105 b observe the table 216 in one of two possible locations,e.g., close to or pushed further away from the couch located in theenvironment 202. As a result, a semantic mapping of the environment 202may include two locations for the table 216 that each has acorresponding probability that may be time-variant. These probabilitiesmay be related, e.g., such that a sum of the two probabilities at anygiven point time is near certainty, i.e., a probability of 1.0.

Similarly, object instances in an environment such as the environment202 may be identified as object instances that are associated with aparticular room or other area of an environment or identified as beingof a class of object instances that are typically associated with aparticular room or area of an environment. Thus, in one example, atoothbrush object instance may be associated with a locationcorresponding to a bathroom and given a probability indicating that thetoothbrush object instance is more likely located in the bathroom thanin a kitchen. In contrast, a coffee pot object instance may beassociated with a location and probability indicating that the coffeepot object instance is likely located in a kitchen, and not in abathroom.

The same may also be true where specific object instances frequentlyappear in a particular room or area of an environment. For example,autonomous robots in the environment 202 of FIG. 2 may frequentlyobserve the plant 218 object instance located in a living room of theenvironment 202. While the plant 218 object instance may move frequentlywithin the living room of the environment 202, because the plant 218 isnever observed outside of that particular room, a probability associatedwith the plant 218 object instance may indicate that the plant 218 ismuch more likely to be located in one or more locations in an area ofthe environment 202 designated as a living room. A probabilityassociated with the plant 218 object instance may therefore indicate arelatively high confidence of the plant 218 being located at an observedlocation, if that location is within the room or area of the environment202 where the plant 218 is frequently observed.

In other examples, a lifelong semantic mapping of an environment mayconsider that two or more object instances are related, such that theproximity of the object instances to one another may be considered indetermining the probability of one of the object instances being inparticular locations. For example, a pair of matching chairs may likelybe located near one another, e.g., as a part of a dining room set, or,as shown in FIG. 2, a television remote 220 object instance may likelybe located near a television 222 object instance.

In some implementations, a system such as the system 100 may determinethat two object instances are related based on characteristics of theobject instances, such as in the case of the television remote 220 andtelevision 222. In other implementations, the system may determine arelationship between two or more object instances based on observationsof the object instances. For example, based on the system obtainingnumerous observations of a particular coffee mug object instance sittingon a particular desk object instance, the system may establish arelationship between the desk and the coffee mug. Thus, the system maydetermine that the coffee mug is more likely to be located in a locationthat is proximate to the desk than in other locations.

A probability associated with a particular location where an objectinstance may be located can also reflect whether the particular locationis a natural location for the object instance within an environment. Forexample, based on characteristics of a class of the object instance, orbased on repeated observations of the object instance, the system maydetermine one or more typical types of locations where an objectinstance is located.

In the environment 202 depicted in FIG. 2, for example, the television222 object instance may be an object that the system knows or learnsover multiple observations is typically mounted to wall of theenvironment 202. Therefore, if an autonomous robot observes thetelevision 222 object instance sitting on a floor in the environment202, the system may assign the observed location with the television222, but assign the location a probability function indicating that thetelevision 222 is unlikely to be in that location for a long period oftime, since it is typically mounted to a wall of the property.Similarly, a television remote 220 may be a class of object that thesystem knows or observes is typically located on a table. Therefore, ifan autonomous robot observes the television remote 220 sitting on afloor of the environment 202, a probability function associated withthat location may indicate that the television remote 220 is unlikely tobe in that location for a long period of time.

The probability associated with the television remote 220 in FIG. 2demonstrates an example probability function that may reflect both thelikelihood of the television remote 220 being located proximate to thetelevision 222, as well as the likelihood of the television remote beinglocated on a floor of the environment 202 for only a short period oftime. Specifically, because the location of the television remote 220 isproximate to the television 222, the semantic mapping indicates that theprobability of the television remote 220 being in that location over thenear future is relatively high. However, this probability decreasesquickly due to the location's being on a floor of the environment 202where the television remote 220 is rarely observed, or where atelevision remote class of objects is unlikely to be located for a longperiod of time.

A semantic mapping of an environment such as the environment 202 mayalso consider the presence of an object instance within the environmentor a particular location within the environment at various times of dayor on various days. For example, a briefcase 224 object instance may beobserved in the environment 202 and a location associated with thebriefcase 224 corresponding to where the briefcase is observed in theenvironment 202. While the briefcase 224 may be observed in the locationfrequently, the system may determine that the briefcase 224 is onlyobserved in that location during certain hours of the day or on certaindays. During other times, the briefcase 224 may be observed in differentlocations at the environment 202, e.g., in a home office area of theenvironment 202 (not shown), or may not be observed in the environment202 at all. A probability associated with the briefcase 224 may reflectthis pattern of observation, such that a semantic mapping of theenvironment 202 indicates a relatively high probability of the briefcase224 being in the particular location during certain times or days, and arelatively low probability of the briefcase 224 being in the particularlocation during other times or on other days.

FIG. 3 depicts an example system 300 in which multiple locations andcorresponding probabilities are assign to a single object instance.Briefly, the system 300 is configured to provide lifelong semanticmapping of an environment 302. The system 300 includes one or moreautonomous robots 105 a, 105 b similar to the autonomous robots 105 a,105 b of FIG. 1A that are configured to make observations at theenvironment 302, and further includes an lifelong semantic mappingengine 310 similar to the lifelong semantic mapping engine 110 of FIGS.1A and 1B. The lifelong semantic mapping engine 310 stores a semanticmapping 325 of the environment 302, and is configured to update thesemantic mapping 325 based on observation data received from the one ormore autonomous robots 305 a, 305 b.

In FIG. 3, the autonomous robots 305 a, 305 b have each identified aparticular coffee mug 312 object instance, and have transmittedobservation data to the lifelong semantic mapping engine 310 indicatingthe respective observations of the coffee mug 312 object instance.Because the observations occur near in time, however, the lifelongsemantic mapping engine 310 may determine that at least one of theobservations of the coffee mug 312 are erroneous. Therefore, thelifelong semantic mapping engine 310 may generate two representations ofthe coffee mug 312 object instance in the semantic mapping 325. Whileshown in FIG. 3 as two distinct representations 312 a, 312 b of thecoffee mug 312 object instance, in other implementations, the semanticmapping 325 may specify the coffee mug 312 as a single object instancewith two distinct probabilities corresponding to the two potentiallocations of the coffee mug 312 object instance in the environment 302.

For example, based on receiving observation data from each of theautonomous robots 305 a, 305 b that identify the coffee mug 312, thelifelong semantic mapping engine 310 may generate two representations312 a, 312 b corresponding to the coffee mug 312 in the semantic mapping325, where each of the representations 312 a, 312 b corresponds to aparticular location where the observation data indicated that the coffeemug 312 was seen in the environment 302. The observation data providedto the lifelong semantic mapping engine 310 may provide additionalinformation, for example, a level of confidence in the identification byeach of the autonomous robots 305 a, 305 b. The lifelong semanticmapping engine 310 may use this form of “voting” from multipleautonomous robots 305 a, 305 b to determine a most likely location ofthe coffee mug 312 in the environment 302.

For example, the observation data received from the autonomous robot 305a may indicate a moderate confidence that the autonomous robot 305 a hasidentified the coffee mug 312 accurately, based on the autonomous robot305 a being fairly far from what it identified as the coffee mug 312 atthe time of the identification, or having a partially obstructed view ofthe object identified as the coffee mug 312 when it made theidentification. In contrast, the autonomous robot 305 b may indicate ahigher confidence that the autonomous robot 305 b has correctlyidentified the coffee mug 312, based on the autonomous robot 305 b beingfairly close to the object when performing the identification, andhaving an unobstructed view of the object. Based on this information,and other factors such as those considered at FIG. 2, the lifelongsemantic mapping engine 310 can estimate a probability 322 a, 322 b ofthe coffee mug 312 being at each of the locations at the environment 302identified by the autonomous robots 305 a, 305 b.

As shown in FIG. 3, for instance, the lifelong semantic mapping engine310 may assign a first probability to the location where the autonomousrobot 305 a observed the coffee mug 312, and a second probability to thelocation where the autonomous robot 305 b observed the coffee mug 312.Based on the observation of the autonomous robot 305 a being anidentification of the coffee mug 312 having a lower confidence than theidentification of the coffee mug 312 by the autonomous robot 305 b, thelifelong semantic mapping engine 310 may assign a probability to therepresentation 312 a of the coffee mug 312 in the semantic mapping 325that is generally lower than the probability assigned to therepresentation 312 b of the coffee mug 312.

An autonomous robot, such as one of the autonomous robots 305 a, 305 bmay later use the semantic mapping 325 including the two representations312 a, 312 b of the coffee mug 312 to locate and interact with thecoffee mug 312 in the environment 302. For example, a user of theenvironment 302 may subsequently provide an instruction to theautonomous robot 305 a to “bring my coffee mug.” The semantic mapping325 including the two representations 312 a, 312 b of the coffee mug 312may be stored at the autonomous robot 305 a, such that the autonomousrobot 305 a can access the semantic mapping 325 and identify aparticular location within the environment 302 where the coffee mug 312is most likely located, based on the probabilities associated with thetwo representations 312 a, 312 b of the coffee mug 312.

Having identified a most likely location of the coffee mug 312, i.e.,the location associated with the representation 312 b of the coffee mug312, the autonomous robot 305 a can navigate to the location of thecoffee mug 312 to retrieve the coffee mug 312. In the event that thecoffee mug 312 is not located in this location, the autonomous robot 305a may then determine to navigate to the location associated with theother representation 312 b of the coffee mug 312 to attempt to retrievethe coffee mug from that location. In this way, the “voting” processenables the autonomous robot 305 a to not only search for the coffee mug312 in its most likely location, but to also have alternate locations inthe environment 302 to search for the coffee mug 312 in the event thatit is not located in the first location searched.

FIG. 4 is a flowchart of an example process 400 for performing lifelongsemantic mapping and using lifelong semantic mapping to control anautonomous robot in an environment. In some implementations, the process400 may be performed by the system 100 of FIGS. 1A and 1B, e.g., by theautonomous robot 105 of FIG. 1B.

The system receives a request that references an object located in anenvironment of the robotic system (402). For example, the autonomousrobot 105 may receive a command from a user of the environment 102 thatspecifies an object instance located in the environment 102. The requestmay request, for instance, that the autonomous robot 105 navigate to thelocation of the object instance, or perform some action with respect tothe object instance, such as retrieve the object instance, move theobject instance to another location within the environment 102, orperform another action with respect to the object instance. In someexamples, the request may be received as a spoken command, or as anotherform of request, e.g., a textual request, request submitted by pressinga particular button of the autonomous robot 105, or may be any otherrequest. In some examples, the autonomous robot 105 may receive therequest from one or more other systems, e.g., over one or more wired orwireless networks, such as when the request is submitted by a user at acomputer that is in communication with the autonomous robot 105.

The system accesses mapping data that indicates, for each of a pluralityof object instances, respective probabilities of the object instancebeing located at one or more locations in the environment (404). Therespective probabilities of the object instance being located at each ofthe one or more locations in the environment are each based at least onan amount of time that has passed since a prior observation of theobject instance was made.

For example, the autonomous robot 105 may access, at the mapping storage156 of autonomous robot 105 or at the lifelong semantic mapping engine110, a semantic mapping of the environment 102. The semantic mapping mayspecify, for each of a plurality of object instances, one or moreprobabilities that are each associated with a particular location withinthe environment 102 and represent the probability of the object instancebeing located at the particular location within the environment 102.Each of the probabilities may be, for example, a probability functionthat indicates the probability or confidence of the object instancebeing located at the particular location over time. Thus, each of theprobabilities specified in the semantic mapping is determined and isdependent at least on an amount of time that has passed since a priorobservation of the object instance was made. Such a probabilitygenerally reflects a decreasing confidence in the location of aparticular object the more time that has passed since the objectinstance was last observed by the autonomous robot 105 or anotherautonomous robot, but may also consider any number of other factors, forexample, as discussed with respect to FIGS. 2 and 3.

The system identifies, from among the plurality of object instances, oneor more particular object instances that correspond to the objectreferenced by the request (406). For example, upon accessing thesemantic mapping of the environment 102, the autonomous robot 105 mayidentify one or more object instances in the semantic mapping thatcorrespond to the object referenced by the request. Each of theidentified object instances may be associated with one or moreprobabilities that each correspond to a particular location within theenvironment 102 and indicate a likelihood of the object instance beinglocated at the particular location within the environment. For example,the autonomous robot 105 may receive a request that identifies a coffeemug, e.g., from a user of the environment 102. The autonomous robot 105may access a semantic mapping of the environment 102, e.g., at themapping storage 156, and may identify from the semantic mapping one ormore coffee mug object instances represented in the semantic mapping.Each of the coffee mug object instances may be associated with one ormore probabilities that each indicate a likelihood of the correspondingcoffee mug object instance being located at a particular location in theenvironment 102.

The system determines, based at least on the mapping data, therespective probabilities of the one or more particular object instancesbeing located at the one or more locations in the environment (408). Forexample, based on the autonomous robot 105 identifying one or moreobject instances in the semantic mapping of the environment 102 thatcorrespond to the object referenced by the request, the autonomous robot105 may determine the probabilities of the corresponding objectinstances being located in one or more locations of the environment 102.Where each of the probabilities is a time-dependent probabilityfunction, for example, the autonomous robot 105 may determine therespective probabilities of the one or more particular object instancesbeing located at one or more locations in the environment 102 bycalculating the value of the function at the time of the request.

For instance, the autonomous robot 105 may identify one or more coffeemug object instances in the semantic mapping of the environment 102,where each of the object instances is associated with one or moreprobabilities that is each a function and corresponds to a particularlocation within the environment 102. The autonomous robot 105, e.g., thecontroller 158 of the autonomous robot 105, may compute a result of eachfunction to determine the probability of the coffee mug being located ateach of the particular locations within the environment 102.

The system selects, based at least on the respective probabilities ofthe one or more particular object instances being located at the one ormore locations in the environment, a particular location in theenvironment where the object referenced by the request is most likelylocated (410). For example, after computing the probabilities of theidentified object instances being at each of the locations correspondingto the probabilities that are associated with each of the objectinstances, the autonomous robot 105 can select a particular location inthe environment 102 where the object referenced by the request is mostlikely located. Selecting the particular location in the environment 102where the object referenced by the request is most likely located maybe, for example, the particular location that has the highest computedprobability for the time of the request.

For example, based on the autonomous robot 105 computing probabilitiesfor each of multiple locations where one or more coffee mug objectinstances may be located, the autonomous robot 105, e.g., the controller158 of the autonomous robot 105, can select a particular location withinthe environment 102 where the coffee mug is mostly likely located, basedon the probabilities. The autonomous robot 105 may select the particularlocation associated with the particular coffee mug object instancehaving the highest probability, indicating the highest confidence of thecoffee mug object instance being in that location.

The system directs a robotic system to navigate to the particularlocation (412). For example, based on the autonomous robot 105 selectinga particular location based on the probabilities associated with the oneor more locations of each of the one or more object instances thatcorrespond to the object referenced in the request, the autonomous robot105 can generate instructions to navigate the autonomous robot 105 tothe particular location. The autonomous robot 105 can use the generatedinstructions to navigate the autonomous robot 105 to the particularlocation. For example, the controller 158 of the autonomous robot 105can use the generated instructions to cause actuators 160 of theautonomous robot 105 to move the autonomous robot 105 to the particularlocation. For instance, based on the autonomous robot 105 selecting aparticular location in the environment 102 where the autonomous robot105 determines that the coffee mug referenced in the request is mostlikely located, the controller 158 of the autonomous robot 105 cangenerate instructions and use those instructions to control theactuators 160 to cause the autonomous robot 105 to navigate to theparticular location.

In addition to navigating to the particular location, the autonomousrobot 105 can also interact with the object referenced by the request,for example, by picking up and retrieving the object, moving the objectto another location, or otherwise interacting with the object. In someexamples, if the autonomous robot 105 navigates to the particularlocation and does not locate the object there, the autonomous robot 105may select a different location within the environment 102, e.g., alocation that is associated with a second-highest probability, and maynavigate to that location to attempt to locate the object referenced bythe request. The autonomous robot 105 may continue this process untilthe autonomous robot 105 locates the referenced object, for example, bylocating the object at one of the locations specific by the mappingdata, or by observing the object within the environment while movingthrough the environment. As the autonomous robot 105 moves through theenvironment 102 and locates or fails to locate the object referenced bythe request, the autonomous robot 105 may communicate information, forexample, to the lifelong semantic mapping engine 110, to update thesemantic mapping of the environment 102 to more accurately reflect thelocation of objects, including the object referenced by the request, inthe semantic mapping.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. For example, various formsof the flows shown above may be used, with steps re-ordered, added, orremoved. Accordingly, other implementations are within the scope of thefollowing claims.

For instances in which the systems and/or methods discussed here maycollect personal information about users, or may make use of personalinformation, the users may be provided with an opportunity to controlwhether programs or features collect personal information, e.g.,information about a user's social network, social actions or activities,profession, preferences, or current location, or to control whetherand/or how the system and/or methods can perform operations morerelevant to the user. In addition, certain data may be anonymized in oneor more ways before it is stored or used, so that personallyidentifiable information is removed. For example, a user's identity maybe anonymized so that no personally identifiable information can bedetermined for the user, or a user's geographic location may begeneralized where location information is obtained, such as to a city,ZIP code, or state level, so that a particular location of a user cannotbe determined. Thus, the user may have control over how information iscollected about him or her and used.

While the foregoing embodiments have been predominantly described withreference to the development or processing of speech inputs for use withapplications installed on user devices, the described features may alsobe used with respect to machines, other devices, robots, or othersystems. For example, the described systems and methods may be used toimprove user interactions with machinery, where the machinery has anassociated computing system, may be used to develop and implement voiceactions for interacting with a robot or system having roboticcomponents, may be used to develop and implement voice actions forinteracting with appliances, entertainment systems, or other devices, ormay be used to develop and implement voice actions for interacting witha vehicle or other transportation system.

Embodiments and all of the functional operations described in thisspecification may be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments may be implemented asone or more computer program products, i.e., one or more modules ofcomputer program instructions encoded on a computer readable medium forexecution by, or to control the operation of, data processing apparatus.The computer readable medium may be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The term “data processing apparatus” encompassesall apparatus, devices, and machines for processing data, including byway of example a programmable processor, a computer, or multipleprocessors or computers. The apparatus may include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, or acombination of one or more of them. A propagated signal is anartificially generated signal, e.g., a machine-generated electrical,optical, or electromagnetic signal that is generated to encodeinformation for transmission to suitable receiver apparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) may be written in any form of programminglanguage, including compiled or interpreted languages, and it may bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program may be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programmay be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification may beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows may also be performedby, and apparatus may also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both.

The essential elements of a computer are a processor for performinginstructions and one or more memory devices for storing instructions anddata. Generally, a computer will also include, or be operatively coupledto receive data from or transfer data to, or both, one or more massstorage devices for storing data, e.g., magnetic, magneto optical disks,or optical disks. However, a computer need not have such devices.Moreover, a computer may be embedded in another device, e.g., a tabletcomputer, a mobile telephone, a personal digital assistant (PDA), amobile audio player, a Global Positioning System (GPS) receiver, to namejust a few. Computer readable media suitable for storing computerprogram instructions and data include all forms of non-volatile memory,media and memory devices, including by way of example semiconductormemory devices, e.g., EPROM, EEPROM, and flash memory devices; magneticdisks, e.g., internal hard disks or removable disks; magneto opticaldisks; and CD ROM and DVD-ROM disks. The processor and the memory may besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments may be implementedon a computer having a display device, e.g., a CRT (cathode ray tube) orLCD (liquid crystal display) monitor, for displaying information to theuser and a keyboard and a pointing device, e.g., a mouse or a trackball,by which the user may provide input to the computer. Other kinds ofdevices may be used to provide for interaction with a user as well; forexample, feedback provided to the user may be any form of sensoryfeedback, e.g., visual feedback, auditory feedback, or tactile feedback;and input from the user may be received in any form, including acoustic,speech, or tactile input.

Embodiments may be implemented in a computing system that includes aback end component, e.g., as a data server, or that includes amiddleware component, e.g., an application server, or that includes afront end component, e.g., a client computer having a graphical userinterface or a Web browser through which a user may interact with animplementation, or any combination of one or more such back end,middleware, or front end components. The components of the system may beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”),e.g., the Internet.

The computing system may include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular embodiments. Certain features that are described in thisspecification in the context of separate embodiments may also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment mayalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination may in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products.

In each instance where an HTML file is mentioned, other file types orformats may be substituted. For instance, an HTML file may be replacedby an XML, JSON, plain text, or other types of files. Moreover, where atable or hash table is mentioned, other data structures (such asspreadsheets, relational databases, or structured files) may be used.

Thus, particular embodiments have been described. Other embodiments arewithin the scope of the following claims. For example, the actionsrecited in the claims may be performed in a different order and stillachieve desirable results.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a system configured to facilitate operation of a robot inan environment, a reference to an object located in the environment ofthe robot; accessing, by the system, mapping data that indicates, foreach of a plurality of object instances, respective probabilities of theobject instance being located at one or more locations in theenvironment, wherein the respective probabilities of the object instancebeing located at each of the one or more locations in the environmentare based at least on an amount of time that has passed since a priorobservation of the object instance was made; identifying, by the systemand from among the plurality of object instances, one or more particularobject instances that correspond to the referenced object; determining,by the system and based at least on the mapping data, the respectiveprobabilities of the one or more particular object instances beinglocated at the one or more locations in the environment; selecting, bythe system and based at least on the respective probabilities of the oneor more particular object instances being located at the one or morelocations in the environment, a particular location in the environmentwhere the referenced object is most likely located; and directing, bythe system, the robot to navigate to the particular location.
 2. Thecomputer-implemented method of claim 1, wherein each of the plurality ofobject instances is an instance of an object class.
 3. Thecomputer-implemented method of claim 1, comprising: controlling, by thesystem, navigation of the robot to the particular location.
 4. Thecomputer-implemented method of claim 1, wherein the respectiveprobabilities of an object instance being located at one or morelocations in the environment are based at least on a temporalcharacteristic associated with an object class of the object instance.5. The computer-implemented method of claim 1, wherein the respectiveprobabilities of an object instance being located at one or morelocations in the environment are based at least on a spatialrelationship between the object instance and one or more other objectinstances.
 6. The computer-implemented method of claim 1, wherein therespective probabilities of an object instance being located at one ormore locations in the environment are based at least on a relationshipbetween an object class of the object instance and an area type of alocation associated with the probability.
 7. The computer-implementedmethod of claim 1, wherein the respective probabilities of an objectinstance being located at one or more locations in the environment arebased at least on a spatiotemporal relationship between an object classof the object instance and a location associated with the probability.8. The computer-implemented method of claim 1, wherein the respectiveprobabilities of an object instance being located at one or morelocations in the environment are based at least on an identificationconfidence associated with one or more prior observations of the objectinstance.
 9. The computer-implemented method of claim 1, wherein themapping data is generated based on observations of a plurality ofrobots.
 10. The computer-implemented method of claim 1, wherein thesystem accesses the mapping data at a cloud-based computing system. 11.A system comprising: one or more processors configured to executecomputer program instructions; and one or more computer-storage mediaencoded with computer programs that, when executed by the one or moreprocessors, cause the system to perform operations comprising:receiving, by a system configured to facilitate operation of a robot inan environment, a reference to an object located in the environment ofthe robot; accessing, by the system, mapping data that indicates, foreach of a plurality of object instances, respective probabilities of theobject instance being located at one or more locations in theenvironment, wherein the respective probabilities of the object instancebeing located at each of the one or more locations in the environmentare based at least on an amount of time that has passed since a priorobservation of the object instance was made; identifying, by the systemand from among the plurality of object instances, one or more particularobject instances that correspond to the referenced object; determining,by the system and based at least on the mapping data, the respectiveprobabilities of the one or more particular object instances beinglocated at the one or more locations in the environment; selecting, bythe system and based at least on the respective probabilities of the oneor more particular object instances being located at the one or morelocations in the environment, a particular location in the environmentwhere the referenced object is most likely located; and directing, bythe system, the robot to navigate to the particular location.
 12. Thesystem of claim 11, wherein each of the plurality of object instances isan instance of an object class.
 13. The system of claim 11, wherein theoperations comprise: controlling, by the system, navigation of the robotto the particular location.
 14. The system of claim 11, wherein therespective probabilities of an object instance being located at one ormore locations in the environment are based at least on a temporalcharacteristic associated with an object class of the object instance.15. The system of claim 11, wherein the respective probabilities of anobject instance being located at one or more locations in theenvironment are based at least on a spatial relationship between theobject instance and one or more other object instances.
 16. The systemof claim 11, wherein the respective probabilities of an object instancebeing located at one or more locations in the environment are based atleast on a relationship between an object class of the object instanceand an area type of a location associated with the probability.
 17. Thesystem of claim 11, wherein the respective probabilities of an objectinstance being located at one or more locations in the environment arebased at least on a spatiotemporal relationship between an object classof the object instance and a location associated with the probability.18. The system of claim 11, wherein the respective probabilities of anobject instance being located at one or more locations in theenvironment are based at least on an identification confidenceassociated with one or more prior observations of the object instance.19. One or more computer-readable devices storing software comprisinginstructions executable by one or more computers which, upon suchexecution, cause the one or more computers to perform operationscomprising: receiving, by a system configured to facilitate operation ofa robot in an environment, a reference to an object located in theenvironment of the robot; accessing, by the system, mapping data thatindicates, for each of a plurality of object instances, respectiveprobabilities of the object instance being located at one or morelocations in the environment, wherein the respective probabilities ofthe object instance being located at each of the one or more locationsin the environment are based at least on an amount of time that haspassed since a prior observation of the object instance was made;identifying, by the system and from among the plurality of objectinstances, one or more particular object instances that correspond tothe referenced object; determining, by the system and based at least onthe mapping data, the respective probabilities of the one or moreparticular object instances being located at the one or more locationsin the environment; selecting, by the system and based at least on therespective probabilities of the one or more particular object instancesbeing located at the one or more locations in the environment, aparticular location in the environment where the referenced object ismost likely located; and directing, by the system, the robot to navigateto the particular location.
 20. The computer-readable device of claim19, wherein each of the plurality of object instances is an instance ofan object class.